In radiology, there is a trend toward more quantitative analysis of medical images. Various data-supported image processing algorithms, which can be optimized based on a machine learning algorithm or, in other words, trained, have been developed for processing medical images. Examples of such image processing algorithms are in particular described in [1]-[4]. An image processing algorithm can, in particular, be embodied to identify, segment or remove a structure. The structure can, in particular, be a bone, an organ, for example a liver, or a tissue structure, for example lung nodules. The image processing algorithm can alternatively or additionally be embodied to determine medical information. The medical information can, in particular, relate to the presence of a symptom and/or a disease, for example a pulmonary embolism. The medical information can, for example, indicate whether and/or with what degree of probability a given symptom and/or a given disease is present in the patient.
Image processing algorithms are typically used on medical images that have been reconstructed in the conventional manner. Conventional image reconstruction algorithms are typically optimized for an evaluation performed by a radiologist by observing the medical image with the naked eye. Herein, the quality of the image reconstruction algorithm is in many cases determined based on subjective quality criteria which are applied to the medical images and which are determined or based on abstract physical and/or statistical variables that are only conditionally related to the quality of the medical image with respect to the medical information to be determined therefrom. The determination of the quality of an image reconstruction algorithm by observing the medical image with the naked eye is often subject to restrictions with respect to the available information content. Restrictions of this kind can, for example, be due to the fact that humans are only able to perceive a limited number of images simultaneously or per time unit and human perceptions are influenced by, to some extent, unconscious, assumptions with respect to spatial and time scales, linearity of the value range and noise and artifacts.